Performance Engineering and Cost Optimization Questions

Engineering practices and trade offs for meeting performance objectives while controlling operational cost. Topics include setting latency and throughput targets and latency budgets; benchmarking profiling and tuning across application database and infrastructure layers; memory compute serialization and batching optimizations; asynchronous processing and workload shaping; capacity estimation and right sizing for compute and storage to reduce cost; understanding cost drivers in cloud environments including network egress and storage tiering; trade offs between real time and batch processing; and monitoring to detect and prevent performance regressions. Candidates should describe measurement driven approaches to optimization and be able to justify trade offs between cost complexity and user experience.

MediumTechnical

0 practiced

Design a benchmarking plan to compare Avro vs Parquet for an analytics workload. The plan should include sample dataset characteristics, metrics to measure (IO throughput, CPU decode time, compression ratio, query latency), test harness design, and how to ensure results are statistically significant and repeatable.

HardTechnical

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A large Spark ETL keeps failing with OOM during shuffle and writes excessive shuffle files. Given a DAG that joins multiple large tables, outline advanced optimizations: adaptive query execution, partitioning strategies, shuffle service tuning, memory fractions, use of off-heap storage, and how to rework the DAG to reduce shuffles. Provide configuration examples and reasoning.

HardTechnical

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Design an experiment to validate that switching the compression codec for Parquet files to Zstd reduces both storage cost and query latency without increasing CPU cost beyond acceptable levels. Include test dataset, metrics, statistical test to compare results, and failure modes to watch for.

MediumSystem Design

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Design a monitoring and alerting approach to detect performance regressions in production data pipelines. Specify which metrics to collect (latency quantiles, throughput, input lag, error rates), how to baseline and detect anomalies, and how to automate short-term remediation (auto-scaling, circuit-breakers) while avoiding alert fatigue.

HardSystem Design

0 practiced

Architect a multi-tenant streaming ingestion system for IoT devices generating 50M events/sec. Requirements: offer sub-100ms E2E latency for high-priority tenants, provide tenant isolation, and meet a cost target per million events. Discuss partitioning, QoS, throttling, storage backend, autoscaling, and trade-offs between cost and latency.

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